Online Deadline-Aware EV Charging Considering State-of-Charge-Dependent Peak Charging Rate

The rapid growth of electric vehicles (EVs) significantly impacts the operation of power grids. Specifically, due to the capacity limit of charging station infrastructure and the inherent uncertainties in EV charging demands, it is imperative to design EV charging scheduling algorithms to meet charging requests more effectively. This paper proposes an online EV charging scheduling method considering the limited charging power supply, which aims to minimize the energy supply cost while ensuring the fulfillment of charging demands. We propose a deadline-aware EV charging queue model to store the energy demands. Based on this queue model, a deadline-differentiated Lyapunov function is designed and incorporated into the Lyapunov optimization framework. To avoid the waste of charging power capacity, we adopt a systematic approach to integrate state-of-charge-dependent peak charging rate into the proposed method without sacrificing computational efficiency. Our method is prediction-free and can adapt to various uncertainties, including random EV arrivals, renewable generation, and electricity prices. Numerical results validate our method’s effectiveness and advantages by comparing it to several state-of-the-art charging scheduling methods.